Roles of Monkey Premotor Neuron Classes in Movement Preparation and Execution

Neurosciences Program, Stanford University, Stanford, California 94305-4075, USA.
Journal of Neurophysiology (Impact Factor: 2.89). 08/2010; 104(2):799-810. DOI: 10.1152/jn.00231.2009
Source: PubMed


Dorsal premotor cortex (PMd) is known to be involved in the planning and execution of reaching movements. However, it is not understood how PMd plan activity-often present in the very same neurons that respond during movement-is prevented from itself producing movement. We investigated whether inhibitory interneurons might "gate" output from PMd, by maintaining high levels of inhibition during planning and reducing inhibition during execution. Recently developed methods permit distinguishing interneurons from pyramidal neurons using extracellular recordings. We extend these methods here for use with chronically implanted multi-electrode arrays. We then applied these methods to single- and multi-electrode recordings in PMd of two monkeys performing delayed-reach tasks. Responses of putative interneurons were not generally in agreement with the hypothesis that they act to gate output from the area: in particular it was not the case that interneurons tended to reduce their firing rates around the time of movement. In fact, interneurons increased their rates more than putative pyramidal neurons during both the planning and movement epochs. The two classes of neurons also differed in a number of other ways, including greater modulation across conditions for interneurons, and interneurons more frequently exhibiting increases in firing rate during movement planning and execution. These findings provide novel information about the greater responsiveness of putative PMd interneurons in motor planning and execution and suggest that we may need to consider new possibilities for how planning activity is structured such that it does not itself produce movement.

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Available from: Matthew T Kaufman, Dec 24, 2013
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    • "We recorded neural activity from motor and premotor cortex during a variety of reaching tasks (Churchland et al. 2006; Kaufman et al. 2010). Neurons showed a broad range of response patterns (Fig. 4) and rarely resembled the predictions of the traditional model (Churchland and Shenoy 2007; Churchland et al. 2010, 2012). "
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    ABSTRACT: The motor cortex was the one of the first cortical areas to be explored electrophysiologically, yet little agreement has emerged regarding its basic response properties. Often it is assumed that single-neuron responses reflect a preference for a particular movement or movement variable. It may be further assumed that movement is generated by (or at least accompanied by) a growing population-level preference for the relevant movement. This view has been attractive because it provides a canonical form for the single neuron, a link between preparatory and movement activity, a way of interpreting the population response, and a platform for designing analyses and couching hypotheses. However, this traditional view yields predictions that are at odds with basic features of the data. We discuss an alternative simplified model, in which outgoing commands are produced by dynamics that generate different output patterns as a function of the initial preparatory state. For reaching tasks, we hypothesized simple quasioscillatory dynamics because they provide a natural basis set for the empirical patterns of muscle activity. The predictions of the dynamical model match the data well at both the single-neuron and population levels, and the quasioscillatory patterns explain many of the otherwise odd features of the neural responses. Copyright © 2014 Cold Spring Harbor Laboratory Press; all rights reserved.
    Cold Spring Harbor Symposia on Quantitative Biology 04/2015; 79. DOI:10.1101/sqb.2014.79.024703
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    • "Sustained preparatory neural activities preceding voluntary movements have been reported in humans (Kornhuber and Deecke, 1965; Libet et al., 1983; Soon et al., 2008) and monkeys (Romo and Schultz, 1987; Kato et al., 1995; Kaufman et al., 2010). Here, however, we further demonstrate that the increased neural activity preceding self-initiated movement is also associated with a heightened sensory sampling (EODR) that precedes movement onset. "
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    ABSTRACT: Cortical activity precedes self-initiated movements by several seconds in mammals; this observation has led into inquiries on the nature of volition. Preparatory neural activity is known to be associated with decision making and movement planning. Self-initiated locomotion has been linked to increased active sensory sampling; however, the precise temporal relationship between sensory acquisition and voluntary movement initiation has not been established. Based on long-term monitoring of sensory sampling activity that is readily observable in freely behaving pulse-type electric fish, we show that heightened sensory acquisition precedes spontaneous initiation of swimming. Gymnotus sp. revealed a bimodal distribution of electric organ discharge rate (EODR) demonstrating down- and up-states of sensory sampling and neural activity; movements only occurred during up-states and up-states were initiated before movement onset. EODR during voluntary swimming initiation exhibited greater trial-to-trial variability than the sound-evoked increases in EODR. The sampling variability declined after voluntary movement onset as previously observed for the neural variability associated with decision making in primates. Spontaneous movements occurred randomly without a characteristic timescale, and no significant temporal correlation was found between successive movement intervals. Using statistical analyses of spontaneous exploratory behaviours and associated preparatory sensory sampling increase, we conclude that electric fish exhibit key attributes of volitional movements, and that voluntary behaviours in vertebrates may generally be preceded by increased sensory sampling. Our results suggest that comparative studies of the neural basis of volition may therefore be possible in pulse-type electric fish, given the substantial homologies between the telencephali of teleost fish and mammals.
    Journal of Experimental Biology 10/2014; 217(Pt 20):3615-28. DOI:10.1242/jeb.105502 · 2.90 Impact Factor
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    • "More specifically, the waveforms of pyramidal neurons tend to be broader and slower than those seen in the most interneurons. Using this classification, several extracellular-recording studies have been able to elucidate roles of pyramidal neurons and interneurons for visual working memory in the prefrontal cortex (Wilson et al., 1994; Rao et al., 1999; Constantinidis and Goldman-Rakic, 2002; Diester and Nieder, 2008; Hussar and Pasternak, 2012), visual attention in V4 (Mitchell et al., 2007), visual perceptual decision-making in the frontal eye field (Ding and Gold, 2011), motor control in the motor and premotor cortices (Isomura et al., 2009; Kaufman et al., 2010), and auditory processing during the passive listening in the auditory cortex (Atencio and Schreiner, 2008; Sakata and Harris, 2009; Ogawa et al., 2011). Interestingly, most of these studies showed differential roles in pyramidal neurons and interneurons. "
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    ABSTRACT: Categorization enables listeners to efficiently encode and respond to auditory stimuli. Behavioral evidence for auditory categorization has been well documented across a broad range of human and non-human animal species. Moreover, neural correlates of auditory categorization have been documented in a variety of different brain regions in the ventral auditory pathway, which is thought to underlie auditory-object processing and auditory perception. Here, we review and discuss how neural representations of auditory categories are transformed across different scales of neural organization in the ventral auditory pathway: from across different brain areas to within local microcircuits. We propose different neural transformations across different scales of neural organization in auditory categorization. Along the ascending auditory system in the ventral pathway, there is a progression in the encoding of categories from simple acoustic categories to categories for abstract information. On the other hand, in local microcircuits, different classes of neurons differentially compute categorical information.
    Frontiers in Neuroscience 06/2014; 8(8):161. DOI:10.3389/fnins.2014.00161 · 3.66 Impact Factor
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